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Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and Environs (ETHER) sample, a comprehensive database encompassing approximately 3.15 million SMBH mass estimates, $\sim$ 20,000 Very-Long Baseline Interferometry (VLBI) radio flux densities, and $\sim$ 36,000 hard X-ray flux densities. This database is designed to identify and optimize target selection for the EHT and its upgrades on the ground and in space. We have identified a Gold Sample (GS) of nearby low-luminosity Active Galactic Nuclei (AGNs) within it that are ideal for studying jet bases and potentially imaging black hole shadows. We observed 27 of these AGNs using the EHT from 2022 to 2024, providing an opportunity to resolve and image accretion flows and jets at resolutions of $\leq$ 100 R$_{\rm g}$. Only a few SMBHs have sufficiently high enough flux density to be imaged at scales of $\leq$ 50 R$_{\rm g}$ with the present EHT. Among these are M87, Sgr A*, NGC4594 (Sombrero/M104), NGC4261, and NGC4374 (Messier 84/M84). Of these, NGC4261, Sombrero, and M84 have been observed and/or are scheduled for deep imaging with EHT+ALMA from 2023 to 2025. Sombrero, NGC4261, M84, NGC4278, and NGC5232 are clearly detected in our EHT+ALMA observations in 2022, indicating that the 230 GHz flux density from the accretion flows is significantly high. Ongoing imaging of the ETHER GS will enable measurements of black hole mass and spin, help constrain General Relativity, and enrich our understanding of jet launching and accretion inflows across a broad multi-parameter space, including black hole mass, spin, accretion rate, and orientation., Comment: 9 pages, 6 figures, 1 table, published in Proceedings of the 16th EVN Symposium, Ed. E. Ros, P. Benke, S.A. Dzib, I. Rottmann, & J.A. Zensus, Bonn: Max-Planck-Institut f\"ur Radioastronomie, 2024, pages 75-84, https://cloud.mpifr-bonn.mpg.de/index.php/s/BkX2CC2Xjn2aKR4

Artificial Kitaev chains based on arrays of quantum dots are promising platforms for realizing Majorana Bound States (MBSs). In a two-site Kitaev chain, it is possible to find these non-Abelian zero-energy excitations at certain points in parameter space (sweet spots). These states, commonly referred to as Poor man's Majorana bound states (PMMs), are challenging to find and stabilize experimentally. In this work, we investigate the evolution of the sweet spots as we increase the number of sites of the Kitaev chain. To this end, we use the Bogoliubov-de Gennes representation to study the excitations of the system, and the scattering matrix and Green functions formalisms to calculate the zero-bias conductance. Our results show that the sweet spots evolve into a region that grows bigger and becomes gradually more protected as the number of sites $N$ increases. Due to the protection of the MBSs, we refer to this region as a topological island. We obtain similar results by considering a realistic spinful model with finite magnetic fields in a chain of normal-superconducting quantum dots. For long chains, $N \geq 20$, we show the emergence of strictly zero-energy plateaus robust against disorder. Finally, we demonstrate that the topological islands can be observed by performing conductance measurements via a quantum dot side-coupled to the Kitaev chain. Our work shows that the fine-tuning required to create and detect PMMs in a 2-site Kitaev chain is significantly relaxed as the length of the chain increases and details how PMMs evolve into MBSs. Our results are consistent with experimental reports for 2 and 3-site chains.

Experimental Physics and Industrial Control System (EPICS) is a software tool that during last years has become relevant as a main framework to deploy distributed control systems in large scientific environments. At the moment, ESS Bilbao uses this middleware to perform the control of their Ion Source Hydrogen Positive (ISHP) project. The implementation of the control system was based on: PXI Real Time controllers using the LabVIEW-RT and LabVIEW-EPICS tools; and RIO devices based on Field-Programmable Gate Array (FPGA) technology. Intended to provide a full compliant EPICS IOCs for RIO devices and to avoid additional efforts on the system maintainability, a migration of the current system to a derivative Red Hat Linux (CentOS) environment has been conducted. This paper presents a real application case study for using the NIRIO EPICS device support (NIRIO-EDS) to give support to the ISHP. Although RIO FPGA configurations are particular solutions for ISHP performance, the NIRIO-EDS has permitted the control and monitoring of devices by applying a well-defined design methodology into the previous FPGA configuration for RIO/FlexRIO devices. This methodology has permitted a fast and easy deployment for the new robust, scalable and maintainable software to support RIO devices into the ISHP control architecture.

We describe the variety of Jordan superalgebras of dimension $4$ whose even part is a Jordan algebra of dimension $1$ or $3$. We prove that the variety is the union of Zariski closures of the orbits of $11$ and $21$ rigid superalgebras, respectively. In both cases, the irreducible components of the varieties are described. Furthermore, we exhibit a four-dimensional solvable rigid Jordan superalgebra, showing that an analogue to the Vergne conjecture for Jordan superalgebras does not hold., Comment: arXiv admin note: text overlap with arXiv:2501.18067

In this paper, a nilpotency criterion is given for finite dimensional alternative superalgebras inspired by the celebrated Engel's Theorem for Lie algebras. As a consequence, a similar result is proved for finite-dimensional special Jordan superalgebras over a field $\mathbb{F}$ of characteristic not $2$, without restrictions on the cardinality of $\mathbb{F}$. In that case, the latter extends Engel's Theorem for Jordan superalgebras constructed by Okunev and Shestakov and it gives a partial positive answer to an open problem announced by Murakami et al. for Jordan superalgebras over finite fields. We also establish some connections between the concepts of graded-nil and nilpotent alternative superalgebras.

We describe the variety of Jordan superalgebras of dimension $4$ whose even part is a Jordan algebra of dimension $2$ over an algebraically closed field $\mathbb{F}$ of characteristic $0$. We prove that the variety has $25$ irreducible components, $24$ of them correspond to the Zariski closure of the $GL_2(\mathbb{F})\times GL_2(\mathbb{F})$-orbits of rigid superalgebras and the other one is the Zariski closure of an union of orbits of an infinite family of superalgebras, none of them being rigid. Furthermore, it is known that the question of the existence of a rigid Jordan algebra whose second cohomology group does not vanish is still an open problem. We solve this problem in the context of superalgebras, showing a four-dimensional rigid Jordan superalgebra whose second cohomology group does not vanish.

Classifying phase transitions is a fundamental and complex challenge in condensed matter physics. This work proposes a framework for identifying quantum phase transitions by combining classical shadows with unsupervised machine learning. We use the axial next-nearest neighbor Ising model as our benchmark and extend the analysis to the Kitaev-Heisenberg model on a two-leg ladder. Even with few qubits, we can effectively distinguish between the different phases of the Hamiltonian models. Moreover, given that we only rely on two-point correlator functions, the classical shadows protocol enables the cost of the analysis to scale logarithmically with the number of qubits, making our approach a scalable and efficient way to study phase transitions in many-body systems., Comment: 12 pages, 16 figures

This note aims to verify a prediction on the total derivative term of the 4D trace anomaly, and the corresponding heat coefficient, for GJMS operators. It stems from the explicit computation of an {\it improved} Casimir (or vacuum) energy on the sphere that takes into account the multiplicative anomaly among the (shifted) Laplacian factors and connects, via the Cappelli-Coste relation, with both the type A central charge and the total derivative term of the 4D trace anomaly. The present heat coefficient computation is based on Juhl's explicit formula for GJMS operators, Gilkey's formula for the integrated heat coefficient of higher-order Laplacians, and the \textit{conformal principle} by Branson and {\O}rsted., Comment: 13 pages, no figures

This paper aims to continue the classification of non-smooth regular curves, but over fields of characteristic three. These curves were originally introduced by Zariski as generic fibers of counterexamples to Bertini's theorem on the variation of singular points of linear series. Such a classification has been introduced by St\"ohr, taking advantage of the equivalent theory of non-conservative function fields, which in turn occurs only over non-perfect fields $K$ of characteristic $p>0$. We propose here a different way of approach, relying on the fact that a non-smooth regular curve in $\mathbb{P}^n_K$ provides a singular curve when viewed inside $\mathbb{P}^n_{K^{1/p}}$. Hence we were naturally induced to the question of characterizing singular curves in $\mathbb{P}^n_{K^{1/p}}$ coming from regular curves in $\mathbb{P}^n_K$. To understand this phenomenon we consider the notion of integrable connections with zero $p$-curvature to extend Katz's version of Cartier's theorem for purely inseparable morphisms, where we solve the above characterization for the slightly general setup of coherent sheaves. Moreover, we also had to introduce some new local invariants attached to non-smooth points, as the differential degree. As an application of the theory developed here, we classify complete, geometrically integral, non-smooth regular curves $C$ of genus $3$, over a separably closed field $K$ of characteristic $3$, whose base extension $C \times_{\operatorname{Spec} K}{\operatorname{Spec} \overline{K}}$ is non-hyperelliptic with normalization having geometric genus $1$.

Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational levels. Despite its advantages, challenges persist in standardizing and implementing programming education effectively across both the higher and secondary education levels in Brazil. To shed light on these issues, we carried out a systematic review of programming teaching methods in the Brazilian context, examining gaps, common techniques, approaches, and action opportunities in programming education. Our findings provide valuable recommendations for educational policymakers and educators to develop effective and updated national policies to teach programming., Comment: 15 pages

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results validate the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity., Comment: This manuscript has been submitted to IEEE journal for possible publication

We study an exactly solvable spin-orbital model that can be regarded as a classical analogue of the celebrated Kitaev honeycomb model and describes interactions between Rydberg atoms on the ruby lattice. We leverage its local and nonlocal symmetries to determine the exact partition function and the static structure factor. A mapping between $S=3/2$ models on the honeycomb lattice and kagome spin Hamiltonians allows us to interpret the thermodynamic properties in terms of a classical kagome spin ice. Partially lifting the symmetries associated with line operators, we obtain a model characterized by immobile excitations, called classical fractons, and a ground state degeneracy that increases exponentially with the length of the system. We formulate a continuum theory that reveals the underlying gauge structure and conserved charges. Extensions of our theory to other lattices and higher-spin systems are suggested., Comment: 13 pages, 8 figures

The data acquired at different scalp EEG electrodes when human subjects are exposed to speech stimuli are highly redundant. The redundancy is partly due to volume conduction effects and partly due to localized regions of the brain synchronizing their activity in response to the stimuli. In a competing talker scenario, we use a recent measure of directed redundancy to assess the amount of redundant information that is causally conveyed from the attended stimuli to the left temporal region of the brain. We observe that for the attended stimuli, the transfer entropy as well as the directed redundancy is proportional to the correlation between the speech stimuli and the reconstructed signal from the EEG signals. This demonstrates that both the rate as well as the rate-redundancy are inversely proportional to the distortion in neural speech tracking. Thus, a greater rate indicates a greater redundancy between the electrode signals, and a greater correlation between the reconstructed signal and the attended stimuli. A similar relationship is not observed for the distracting stimuli., Comment: Accepted for IEEE Data Compression Conference

Compound AI Systems, integrating multiple interacting components like models, retrievers, and external tools, have emerged as essential for addressing complex AI tasks. However, current implementations suffer from inefficient resource utilization due to tight coupling between application logic and execution details, a disconnect between orchestration and resource management layers, and the perceived exclusiveness between efficiency and quality. We propose a vision for resource-efficient Compound AI Systems through a declarative workflow programming model and an adaptive runtime system for dynamic scheduling and resource-aware decision-making. Decoupling application logic from low-level details exposes levers for the runtime to flexibly configure the execution environment and resources, without compromising on quality. Enabling collaboration between the workflow orchestration and cluster manager enables higher efficiency through better scheduling and resource management. We are building a prototype system, called Murakkab, to realize this vision. Our preliminary evaluation demonstrates speedups up to $\sim 3.4\times$ in workflow completion times while delivering $\sim 4.5\times$ higher energy efficiency, showing promise in optimizing resources and advancing AI system design.

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO has been accompanied by increased hardware complexity and array sizes, causing system power consumption to rise as a result. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. This \emph{tri-hybrid} approach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adding new layer for signal processing. We provide a comprehensive analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We also discuss several design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters., Comment: IEEE Transactions on Communications (invited)

We theoretically designed and experimentally demonstrated a mechanism to protect a spatially segregated mixed light-matter state, known as intercavity exciton-polariton in strongly coupled optical cavities. This excitation, shared across the coupled cavity array, exhibits remarkable robustness over a wide momentum range, without compromising photon-exciton mixing or the spatial separation of its photonic and excitonic components, which also enables a tunable heavy mass. Additionally, we unveil a direct connection between the transparency window, characteristic of slow-light experiments, and the protection of the intercavity polariton nature. Both phenomena originate from the strategic design of an energy-level landscape featuring a $\Lambda$-scheme, opening new avenues for exploring and utilizing these unique optical excitations in advanced photonic applications., Comment: 5+2 pages, 5 figures. Comments are welcome

Activities in underwater environments are paramount in several scenarios, which drives the continuous development of underwater image enhancement techniques. A major challenge in this domain is the depth at which images are captured, with increasing depth resulting in a darker environment. Most existing methods for underwater image enhancement focus on noise removal and color adjustment, with few works dedicated to brightness enhancement. This work introduces a novel unsupervised learning approach to underwater image enhancement using a diffusion model. Our method, called UDBE, is based on conditional diffusion to maintain the brightness details of the unpaired input images. The input image is combined with a color map and a Signal-Noise Relation map (SNR) to ensure stable training and prevent color distortion in the output images. The results demonstrate that our approach achieves an impressive accuracy rate in the datasets UIEB, SUIM and RUIE, well-established underwater image benchmarks. Additionally, the experiments validate the robustness of our approach, regarding the image quality metrics PSNR, SSIM, UIQM, and UISM, indicating the good performance of the brightness enhancement process. The source code is available here: https://github.com/gusanagy/UDBE., Comment: Paper presented at ICMLA 2024

NCORES was a large observing program on the ESO HARPS spectrograph, dedicated to measuring the masses of Neptune-like and smaller transiting planets discovered by the TESS satellite using the radial velocity technique. This paper presents an overview of the programme, its scientific goals and published results, covering 35 planets in 18 planetary systems. We present spectrally derived stellar characterisation and mass constraints for five additional TOIs where radial velocity observations found only marginally significant signals (TOI-510.01, $M_p=1.08^{+0.58}_{-0.55}M_\oplus$), or found no signal (TOIs 271.01, 641.01, 697.01 and 745.01). A newly detected non-transiting radial velocity candidate is presented orbiting TOI-510 on a 10.0d orbit, with a minimum mass of $4.82^{+1.29}_{-1.26}M_\oplus$, although uncertainties on the system architecture and true orbital period remain. Combining the NCORES sample with archival known planets we investigate the distribution of planet masses and compositions around and below the radius gap, finding that the population of planets below the gap is consistent with a rocky composition and ranges up to a sharp cut-off at $10M_\oplus$. We compare the observed distribution to models of pebble- and planetesimal-driven formation and evolution, finding good broad agreement with both models while highlighting interesting areas of potential discrepancy. Increased numbers of precisely measured planet masses in this parameter space are required to distinguish between pebble and planetesimal accretion., Comment: Accepted to MNRAS

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

Ensembling is commonly regarded as an effective way to improve the general performance of models in machine learning, while also increasing the robustness of predictions. When it comes to algorithmic fairness, heterogeneous ensembles, composed of multiple model types, have been employed to mitigate biases in terms of demographic attributes such as sex, age or ethnicity. Moreover, recent work has shown how in multi-class problems even simple homogeneous ensembles may favor performance of the worst-performing target classes. While homogeneous ensembles are simpler to implement in practice, it is not yet clear whether their benefits translate to groups defined not in terms of their target class, but in terms of demographic or protected attributes, hence improving fairness. In this work we show how this simple and straightforward method is indeed able to mitigate disparities, particularly benefiting under-performing subgroups. Interestingly, this can be achieved without sacrificing overall performance, which is a common trade-off observed in bias mitigation strategies. Moreover, we analyzed the interplay between two factors which may result in biases: sub-group under-representation and the inherent difficulty of the task for each group. These results revealed that, contrary to popular assumptions, having balanced datasets may be suboptimal if the task difficulty varies between subgroups. Indeed, we found that a perfectly balanced dataset may hurt both the overall performance and the gap between groups. This highlights the importance of considering the interaction between multiple forces at play in fairness., Comment: 12 pages, 6 figures

Suspensions of self-propelled objects represent a novel paradigm in colloidal science. In such active baths traditional concepts, such as Brownian motion, fluctuation-dissipation relations, and work extraction from heat reservoirs, must be extended beyond the conventional framework of thermal baths. Unlike thermal baths, which are characterized by a single parameter, the temperature, the fundamental descriptors of an active bath remain elusive, especially in confined environments. In this study, buoyant, passive tracers are employed as generalized probes to investigate an active bath comprising motile bacteria confined within a droplet. We demonstrate that momentum transfer from the bath to the tracer can be effectively described as colored noise, characterized by temporal memory and an enhanced effective diffusivity significantly larger compared to thermal Brownian motion values. Using a stochastic analytical framework, we extract the temporal memory and diffusivity parameters that define such an active bath. Notably, the diffusivity scales linearly with bacterial concentration, modulated by a factor representing the role of confinement, expressed as the ratio of the confining radius to the probe radius. This finding, while still awaiting a complete theoretical explanation, offers new insights into the transport properties of confined active baths and paves the way for a deeper understanding of active emulsions driven by confined active matter.

We investigate the pseudospectrum of a Schwarzschild-like spacetime within the framework of black hole perturbation theory to analyze a counterintuitive assertion regarding the instability of quasinormal modes. Recent findings suggest that random perturbations to the effective potential associated with gravitational waves may enhance the stability of the underlying wave operator, thereby yielding a stable spectrum of randomly displaced quasinormal modes. Given the unphysical nature of such random perturbations, this work examines these findings within a spacetime that inherently exhibits a perturbed quasinormal spectrum. We find that, in contrast to the QNM spectrum of the Schwarzschild spacetime under random perturbations, the quasinormal spectrum of a Schwarzschild-like black hole deformed through a physically motivated implementation of the Rezzolla-Zhidenko parametrization is unstable. In particular, we show that the pseudospectra of these Schwarzschild-like black holes do not display the typical features associated with wave operators that yield stable quasinormal spectra. We corroborate our findings by computing the quasinormal spectra when additional (ad-hoc) deformations are added to the effective potential of the Rezzolla-Zhidenko black hole. We also argue that when multiple perturbation sources are present, identifying the origin of the instability may be difficult., Comment: v2:12 pages, 4 figures, references added

Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.

Rigid registration aims to determine the translations and rotations necessary to align features in a pair of images. While recent machine learning methods have become state-of-the-art for linear and deformable registration across subjects, they have demonstrated limitations when applied to longitudinal (within-subject) registration, where achieving precise alignment is critical. Building on an existing framework for anatomy-aware, acquisition-agnostic affine registration, we propose a model optimized for longitudinal, rigid brain registration. By training the model with synthetic within-subject pairs augmented with rigid and subtle nonlinear transforms, the model estimates more accurate rigid transforms than previous cross-subject networks and performs robustly on longitudinal registration pairs within and across magnetic resonance imaging (MRI) contrasts., Comment: 5 pages, 4 figures, 1 table, rigid image registration, deep learning, longitudinal analysis, neuroimaging, accepted by the IEEE International Symposium on Biomedical Imaging

Splatting-based 3D reconstruction methods have gained popularity with the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel views. These methods commonly resort to using exponential family functions, such as the Gaussian function, as reconstruction kernels due to their anisotropic nature, ease of projection, and differentiability in rasterization. However, the field remains restricted to variations within the exponential family, leaving generalized reconstruction kernels largely underexplored, partly due to the lack of easy integrability in 3D to 2D projections. In this light, we show that a class of decaying anisotropic radial basis functions (DARBFs), which are non-negative functions of the Mahalanobis distance, supports splatting by approximating the Gaussian function's closed-form integration advantage. With this fresh perspective, we demonstrate up to 34% faster convergence during training and a 15% reduction in memory consumption across various DARB reconstruction kernels, while maintaining comparable PSNR, SSIM, and LPIPS results. We will make the code available., Comment: Link to the project page: https://randomnerds.github.io/darbs.github.io/

Nodal-line semimetals (NLSMs) harbor a variety of novel physical properties owing to the particularities of the band degeneracies that characterize the spectrum of these materials. In symmetry-enforced NLSMs, band degeneracies, being imposed by symmetries, are robust to arbitrarily strong perturbations that preserve the symmetries. We investigate the effects of electron-electron interactions on a recently proposed vacancy-engineered NLSM known as holey graphene. Using mean-field calculations and quantum Monte Carlo simulation, we show that the Hubbard model on the depleted holey-graphene lattice at half-filling exhibits a transition from a NLSM to an insulating antiferromagnetic phase for an arbitrarily weak repulsive interaction $U$. In contrast to the semi-metal-insulator transition in the pristine honeycomb lattice, which occurs at a finite critical value of $U$, in the depleted lattice, the transition at $U=0$ is associated with a van Hove singularity arising from the crossing of accidental nodal lines enforced by symmetry. We also employ linear spin wave theory (LSWT) to the effective Heisenberg model in the strong-coupling limit and obtain the global antiferromagnetic order parameter $m_{\rm AFM} \approx 0.146$. The order parameters from both QMC and LSWT agree quantitatively. Our findings indicate that vacancy engineering offers an effective way to tailor the magnetic properties of quantum materials., Comment: 13 pages, 14 figures

We present measurements of large-scale cosmic microwave background (CMB) E-mode polarization from the Cosmology Large Angular Scale Surveyor (CLASS) 90 GHz data. Using 115 det-yr of observations collected through 2024 with a variable-delay polarization modulator, we achieved a polarization sensitivity of $78\,\mathrm{\mu K\,arcmin}$, comparable to Planck at similar frequencies (100 and 143 GHz). The analysis demonstrates effective mitigation of systematic errors and addresses challenges to large-angular-scale power recovery posed by time-domain filtering in maximum-likelihood map-making. A novel implementation of the pixel-space transfer matrix is introduced, which enables efficient filtering simulations and bias correction in the power spectrum using the quadratic cross-spectrum estimator. Overall, we achieved an unbiased time-domain filtering correction to recover the largest angular scale polarization, with the only power deficit, arising from map-making non-linearity, being characterized as less than $3\%$. Through cross-correlation with Planck, we detected the cosmic reionization at $99.4\%$ significance and measured the reionization optical depth $\tau=0.053^{+0.018}_{-0.019}$, marking the first ground-based attempt at such a measurement. At intermediate angular scales ($\ell>30$), our results, both independently and in cross-correlation with Planck, remain fully consistent with Planck's measurements., Comment: 24 pages, 19 figures, 3 tables; submitted to ApJ

This study critically evaluates gender assignment methods within academic contexts, employing a comparative analysis of diverse techniques, including a SVM classifier, gender-guesser, genderize.io, and a Cultural Consensus Theory based classifier. Emphasizing the significance of transparency, data sources, and methodological considerations, the research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database. The results reveal a substantial reduction in the number of individuals with unknown gender compared to traditional methods relying on INE data. The nuanced differences in gender distribution underscore the importance of methodological choices in gender studies, urging for transparent, comprehensive, and freely accessible methods to enhance the accuracy and reliability of gender assignment in academic research. After reevaluating the problem of gender imbalances in the doctoral system we can conclude that it's still evident although the trend is clearly set for its reduction. Finaly, specific problems related to some disciplines, including STEM fields and seniority roles are found to be worth of attention in the near future.

The Short-Baseline Near Detector (SBND) is one of three Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, as part of the Short-Baseline Neutrino (SBN) Program. The detector is currently being commissioned and is expected to take neutrino data this year. SBND is characterized by superb imaging capabilities and will record over a million neutrino interactions per year. Thanks to its unique combination of measurement resolution and statistics, SBND will carry out a rich program of neutrino interaction measurements and novel searches for physics beyond the Standard Model (BSM). It will enable the potential of the overall SBN sterile neutrino program by performing a precise characterization of the unoscillated event rate, and constraining BNB flux and neutrino-argon cross-section systematic uncertainties. In this proceedings, the physics reach, current status, and future prospects of SBND are discussed and early data is presented., Comment: 6 pages, 5 figures, ICHEP 2024 Proceedings

Alzheimer's Disease is a neurodegenerative condition characterized by dementia and impairment in neurological function. The study primarily focuses on the individuals above age 40, affecting their memory, behavior, and cognitive processes of the brain. Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis, specifically focusing on the Convolutional Neural Network, Bayesian Convolutional Neural Network, and the U-net model with the Open Access Series of Imaging Studies brain MRI dataset. Besides, to ensure robustness and reliability in the model evaluations, we address the challenge of imbalance in data. We then perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency. This comparative analysis would shed light on the future role of AI in revolutionizing AD diagnostics but also paved ways for future innovation in medical imaging and the management of neurodegenerative diseases.

The theory of Leonard triples is applied to the derivation of normalized scalar products of on-shell and off-shell Bethe states generated from a Leonard pair. The scalar products take the form of linear combinations of $q$-Racah polynomials with coefficients depending on the off-shell parameters. Upon specializations, explicit solutions for the corresponding Belliard-Slavnov linear systems are obtained. It implies the existence of a determinant formula in terms of inhomogeneous Bethe roots for the $q$-Racah polynomials. Also, a set of relations that determines solutions (Bethe roots) of the corresponding Bethe equations of inhomogeneous type in terms of solutions of Bethe equations of homogenous type is obtained., Comment: 26pp

We report the discovery of a new binary galaxy cluster merger, the Champagne Cluster (RM J130558.9+263048.4), using a detection method that identifies dynamically active clusters in the redMaPPer SDSS DR8 photometric galaxy cluster catalog. The Champagne Cluster exhibits the classic X-ray morphology of a post-pericenter dissociative galaxy cluster merger: an X-ray peak located between two galaxy overdensities at the same redshift. We conducted a Keck/DEIMOS survey and obtained redshifts for 103 member galaxies. The redshift analysis indicates a relative velocity of 411 $\pm$ 180 km/s between the two subclusters, which suggests that the merger is happening near the plane of the sky. We used cosmological simulations to find analogous systems to constrain the time since pericenter (74-250 Myr) and the angle the merger axis makes with the plane of the sky (62$^\circ$-90$^\circ$) at the 68$\%$ confidence level. We estimated the bulk temperature (8.20 $\pm 1.2$ keV) and total X-ray luminosity (5.2 $\pm$ 0.8 $\times$ $10^{44}$ erg $\times$ $s^{-1}$) of the intracluster medium using $\textit{Chandra}$ archival data.

This study explores the origins of cosmic rays and their secondary messengers, focusing on the potential role of four BL Lacs W Comae, 1ES 1959+650, PKS 2005-489, and PKS 2155-304 as potential sources of astrophysical neutrinos and gamma rays. We analyzed a single-zone model to understand the interactions between high-energy protons and ambient photons within blazar jets, leading to neutrino production observables and gamma-ray emission. This modeling contextualizes the emissions within multiwavelength observations and evaluates the capabilities of the next-generation Cherenkov Telescope Array Observatory (CTAO) in detecting these emissions. Our estimations suggest that these sources could be effective emitters of CRs, highlighting the need for future multimessenger observations to further investigate and constrain this class of sources., Comment: 34 pages, 12 figures

Compute Express Link (CXL) is widely-supported interconnect standard that promises to enable memory disaggregation in data centers. CXL allows for memory pooling, which can be used to create a shared memory space across multiple servers. However, CXL does not specify how to actually build a memory pool. Existing proposals for CXL memory pools are expensive, as they require CXL switches or large multi-headed devices. In this paper, we propose a new design for CXL memory pools that is cost-effective. We call these designs Octopus topologies. Our design uses small CXL devices that can be made cheaply and offer fast access latencies. Specifically, we propose asymmetric CXL topologies where hosts connect to different sets of CXL devices. This enables pooling and sharing memory across multiple hosts even as each individual CXL device is only connected to a small number of hosts. Importantly, this uses hardware that is readily available today. We also show the trade-off in terms of CXL pod size and cost overhead per host. Octopus improves the Pareto frontier defined by prior policies, e.g., offering to connect 3x as many hosts at 17% lower cost per host.

AI algorithms have become valuable in aiding professionals in healthcare. The increasing confidence obtained by these models is helpful in critical decision demands. In clinical dermatology, classification models can detect malignant lesions on patients' skin using only RGB images as input. However, most learning-based methods employ data acquired from dermoscopic datasets on training, which are large and validated by a gold standard. Clinical models aim to deal with classification on users' smartphone cameras that do not contain the corresponding resolution provided by dermoscopy. Also, clinical applications bring new challenges. It can contain captures from uncontrolled environments, skin tone variations, viewpoint changes, noises in data and labels, and unbalanced classes. A possible alternative would be to use transfer learning to deal with the clinical images. However, as the number of samples is low, it can cause degradations on the model's performance; the source distribution used in training differs from the test set. This work aims to evaluate the gap between dermoscopic and clinical samples and understand how the dataset variations impact training. It assesses the main differences between distributions that disturb the model's prediction. Finally, from experiments on different architectures, we argue how to combine the data from divergent distributions, decreasing the impact on the model's final accuracy., Comment: 10 pages, 1 figure

Imaging atmospheric Cherenkov telescopes (IACTs) detect gamma rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the gamma rays hit the atmosphere. At low energies, the limited amount of Cherenkov light produced typically implies that the event is registered by one IACT only. Such events are called monoscopic events, and their analysis is particularly difficult. Challenges include the reconstruction of the event's arrival direction, energy, and the rejection of background events. Here, we present a set of improvements, including a machine-learning algorithm to determine the correct orientation of the image, an intensity-dependent selection cut that ensures optimal performance, and a collection of new image parameters. To quantify these improvements, we use the central telescope of the H.E.S.S. IACT array. Knowing the correct image orientation, which corresponds to the arrival direction of the photon in the camera frame, is especially important for the angular reconstruction, which could be improved in resolution by 57% at 100 GeV. The event selection cut, which now depends on the total measured intensity of the events, leads to a reduction of the low-energy threshold for source analyses by ~50%. The new image parameters characterize the intensity and time distribution within the recorded images and complement the traditionally used Hillas parameters in the machine learning algorithms. We evaluate their importance to the algorithms in a systematic approach and carefully evaluate associated systematic uncertainties. We find that including subsets of the new variables in machine-learning algorithms improves the reconstruction and background rejection, resulting in a sensitivity improved by 41% at the low-energy threshold.

The spectral structure of a photonic reservoir shapes radiation phenomena for embedded quantum emitters. We implement an all-optical analogue to study such an effect, particularly to observe the non-Markovian radiation dynamics of an emitter coupled to two-dimensional structured reservoirs. Its dynamics is simulated by light propagating through a photonic lattice, acting as a reservoir for an adjacent waveguide that mimics a coupled quantum emitter. We study radiation dynamics in square and Lieb lattices under different coupling regimes and observe how the flat band properties of the Lieb lattice significantly enhances light-matter coupling and non-Markovianity. Our platform opens a path for the experimental exploration of single photon quantum optical phenomena in structured reservoirs to enhance light-matter interactions., Comment: 5pages, 5 figures

In a rapidly evolving knowledge landscape and the increasing adoption of large language models, a need has emerged to keep these models continuously updated with current events. While existing benchmarks evaluate general factual recall, they often overlook two critical aspects: the ability of models to integrate evolving knowledge through continual learning and the significant regional disparities in their performance. To address these gaps, we introduce the Timely Events Benchmark (TiEBe), a dataset containing over 11,000 question-answer pairs focused on globally and regionally significant events. TiEBe leverages structured retrospective data from Wikipedia, enabling continuous updates to assess LLMs' knowledge of evolving global affairs and their understanding of events across different regions. Our benchmark demonstrates that LLMs exhibit substantial geographic disparities in factual recall, emphasizing the need for more balanced global knowledge representation. Furthermore, TiEBe serves as a tool for evaluating continual learning strategies, providing insights into models' ability to acquire new information without forgetting past knowledge.

This paper introduces a novel Bayesian approach for variable selection in high-dimensional and potentially sparse regression settings. Our method replaces the indicator variables in the traditional spike and slab prior with continuous, Beta-distributed random variables and places half Cauchy priors over the parameters of the Beta distribution, which significantly improves the predictive and inferential performance of the technique. Similar to shrinkage methods, our continuous parameterization of the spike and slab prior enables us explore the posterior distributions of interest using fast gradient-based methods, such as Hamiltonian Monte Carlo (HMC), while at the same time explicitly allowing for variable selection in a principled framework. We study the frequentist properties of our model via simulation and show that our technique outperforms the latest Bayesian variable selection methods in both linear and logistic regression. The efficacy, applicability and performance of our approach, are further underscored through its implementation on real datasets.

A hybrid framework integrating the Virtual Element Method (VEM) with deep learning is presented as an initial step toward developing efficient and flexible numerical models for one-dimensional Euler-Bernoulli beams. The primary aim is to explore a data-driven surrogate model capable of predicting displacement fields across varying material and geometric parameters while maintaining computational efficiency. Building upon VEM's ability to handle higher-order polynomials and non-conforming discretizations, the method offers a robust numerical foundation for structural mechanics. A neural network architecture is introduced to separately process nodal and material-specific data, effectively capturing complex interactions with minimal reliance on large datasets. To address challenges in training, the model incorporates Sobolev training and GradNorm techniques, ensuring balanced loss contributions and enhanced generalization. While this framework is in its early stages, it demonstrates the potential for further refinement and development into a scalable alternative to traditional methods. The proposed approach lays the groundwork for advancing numerical and data-driven techniques in beam modeling, offering a foundation for future research in structural mechanics.

Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their collapse and internal dynamics are codified in well-known scaling relations. Cosmological hydrodynamical simulations play an important role, providing insights into the 3D distribution of matter and galaxy formation mechanisms, as well as validating methods to infer the properties of real objects. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs," based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modeling them via S\'ersic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.

We present Seg-TTO, a novel framework for zero-shot, open-vocabulary semantic segmentation (OVSS), designed to excel in specialized domain tasks. While current open vocabulary approaches show impressive performance on standard segmentation benchmarks under zero-shot settings, they fall short of supervised counterparts on highly domain-specific datasets. We focus on segmentation-specific test-time optimization to address this gap. Segmentation requires an understanding of multiple concepts within a single image while retaining the locality and spatial structure of representations. We propose a novel self-supervised objective adhering to these requirements and use it to align the model parameters with input images at test time. In the textual modality, we learn multiple embeddings for each category to capture diverse concepts within an image, while in the visual modality, we calculate pixel-level losses followed by embedding aggregation operations specific to preserving spatial structure. Our resulting framework termed Seg-TTO is a plug-in-play module. We integrate Seg-TTO with three state-of-the-art OVSS approaches and evaluate across 22 challenging OVSS tasks covering a range of specialized domains. Our Seg-TTO demonstrates clear performance improvements across these establishing new state-of-the-art. Code: https://github.com/UlinduP/SegTTO.

It has been recently shown that cosmological models with scale-dependent primordial non-Gaussianities (sPNG) could provide a possible path to solve current cosmic tensions. Moreover, it has been pointed out that some of these models might mimic the effects of Warm Dark Matter (WDM) for several observables at low redshift. Here, we confirm the qualitative similarity of the matter power spectrum for sPNG and WDM models, but also point out differences in the halo mass function and void size function. We then jointly simulate WDM and sPNG together. Such simulations allow us to demonstrate that the joint impact of WDM and sPNG is close to the linear superposition of their respective effects at low redshift, at the percent level. We finally propose a model with mixed hot and cold dark matter together with sPNG, that reproduces the $\Lambda$CDM power spectrum at redshifts $z \leq 3$ but is still distinct in terms of halo statistics., Comment: 13 pages, 7 figures, comments welcome :)

We investigate the feasibility and accuracy of a machine learning model to predict the dynamics of a gas pocket that is formed when a breaking wave impacts on a solid wall. The proposed ML model is based on the convolutional long short-term memory structure and is trained with experimental data. In particular, it takes as input two high-speed camera snapshots before impact and produces as output six scalars that describe the dynamics of the gas pocket. The experiments are performed in a wave flume, where we use solitons -- in combination with a bathymetry profile -- to generate wave breaking close to a solid wall which is instrumented with dynamic pressure sensors. By varying the water depth $h_\ell$ and the parameter $\alpha = A/h_\ell$, where $A$ is the soliton wave amplitude, we are able to generate a family of unique breaking waves with different gas pocket sizes and wave kinematics. In this so-called phase space of wave generation ($h_\ell$, $\alpha$), we perform experiments on 67 different wave states that form our dataset. Experimentally, we find that the frequency of oscillation of the gas pocket can be attributed to the initial volume of gas plus a geometric correction and that the maximum and minimum pressures are qualitatively well captured by the one-dimensional Bagnold model. In terms of the ML model, we compare its performance to the experimental data and find that the model quantitatively reproduces the trends found in the experiments -- in particular for the maximum and minimum pressure in the gas pocket and the frequency of oscillation., Comment: 24 pages, 18 figures, Ocean Eng. (In press)

The paper is devoted to two-weight estimates for the fractional maximal operators $\mathcal{M}^\alpha$ on general probability spaces equipped with a tree-like structure. For given $1

Complex systems are characterized by nonlinear dynamics, multi-level interactions, and emergent collective behaviors. Traditional analyses that focus solely on pairwise interactions often oversimplify these systems, neglecting the higher-order interactions critical for understanding their full collective dynamics. Recent advances in multivariate information theory provide a principled framework for quantifying these higher-order interactions, capturing key properties such as redundancy, synergy, shared randomness, and collective constraints. However, two major challenges persist: accurately estimating joint entropies and addressing the combinatorial explosion of interacting terms. To overcome these challenges, we introduce THOI (Torch-based High-Order Interactions), a novel, accessible, and efficient Python library for computing high-order interactions in continuous-valued systems. THOI leverages the well-established Gaussian copula method for joint entropy estimation, combined with state-of-the-art batch and parallel processing techniques to optimize performance across CPU, GPU, and TPU environments. Our results demonstrate that THOI significantly outperforms existing tools in terms of speed and scalability. For larger systems, where exhaustive analysis is computationally impractical, THOI integrates optimization strategies that make higher-order interaction analysis feasible. We validate THOI accuracy using synthetic datasets with parametrically controlled interactions and further illustrate its utility by analyzing fMRI data from human subjects in wakeful resting states and under deep anesthesia. Finally, we analyzed over 900 real-world and synthetic datasets, establishing a comprehensive framework for applying higher-order interaction (HOI) analysis in complex systems., Comment: 22 pages, 6 figures

The rising demand for generative large language models (LLMs) poses challenges for thermal and power management in cloud datacenters. Traditional techniques often are inadequate for LLM inference due to the fine-grained, millisecond-scale execution phases, each with distinct performance, thermal, and power profiles. Additionally, LLM inference workloads are sensitive to various configuration parameters (e.g., model parallelism, size, and quantization) that involve trade-offs between performance, temperature, power, and output quality. Moreover, clouds often co-locate SaaS and IaaS workloads, each with different levels of visibility and flexibility. We propose TAPAS, a thermal- and power-aware framework designed for LLM inference clusters in the cloud. TAPAS enhances cooling and power oversubscription capabilities, reducing the total cost of ownership (TCO) while effectively handling emergencies (e.g., cooling and power failures). The system leverages historical temperature and power data, along with the adaptability of SaaS workloads, to: (1) efficiently place new GPU workload VMs within cooling and power constraints, (2) route LLM inference requests across SaaS VMs, and (3) reconfigure SaaS VMs to manage load spikes and emergency situations. Our evaluation on a large GPU cluster demonstrates significant reductions in thermal and power throttling events, boosting system efficiency.